Predicting pesticide dissipation half-life intervals in plants with machine learning models

Diabetic peripheral neuropathy detection of type 2 diabetes using machine learning from TCM features: a cross-sectional study

AIMS

Diabetic peripheral neuropathy (DPN) is the most common complication of diabetes mellitus. Early identification of individuals at high risk of DPN is essential for successful early intervention. Traditional Chinese medicine (TCM) tongue diagnosis, one of the four diagnostic methods, lacks specific algorithms for TCM symptoms and tongue features. This study aims to develop machine learning (ML) models based on TCM to predict the risk of diabetic peripheral neuropathy (DPN) in patients with type 2 diabetes mellitus (T2DM).

METHODS

A total of 4723 patients were included in the analysis (4430 with T2DM and 293 with DPN). TFDA-1 was used to obtain tongue images during a questionnaire survey. LASSO (least absolute shrinkage and selection operator) logistic regression model with fivefold cross-validation was used to select imaging features, which were then screened using best subset selection. The synthetic minority oversampling technique (SMOTE) algorithm was applied to address the class imbalance and eliminate possible bias. The area under the receiver operating characteristic curve (AUC) was used to evaluate the model's performance. Four ML algorithms, namely logistic regression (LR), random forest (RF), support vector classifier (SVC), and light gradient boosting machine (LGBM), were used to build predictive models for DPN. The importance of covariates in DPN was ranked using classifiers with better performance.

RESULTS

The RF model performed the best, with an accuracy of 0.767, precision of 0.718, recall of 0.874, F-1 score of 0.789, and AUC of 0.77. With a value of 0.879, the LGBM model appeared to be the best regarding recall Age, sweating, dark red tongue, insomnia, and smoking were the five most significant RF features. Age, yellow coating, loose teeth, smoking, and insomnia were the five most significant features of the LGBM model.

CONCLUSIONS

This cross-sectional study demonstrates that the RF and LGBM models can screen for high-risk DPN in T2DM patients using TCM symptoms and tongue features. The identified key TCM-related features, such as age, tongue coating, and other symptoms, may be advantageous in developing preventative measures for T2DM patients.

First report on regression-based QSAR addressing pesticide dissipation half-life in plants: A step towards sustainable public health

The excessive use of pesticides (an important group of chemicals) in the agricultural as well as public sectors raises a health concern. Pesticides affect humans and other living organisms via the food chain. Therefore, it is very necessary to calculate the dissipation half-life of pesticides in plants. Experimental prediction of pesticide dissipation half-lives requires complex environmental conditions, high cost, and a long time. Thus, in-silico half-life predictions are suitable and the best alternative. Herein, a total of six PLS (partial least squares) models namely, M1 (overall), M2 (fruit), M3 (plant interior), M4 (leaf), M5 (plant surface), and M6 (whole plant) alongside two MLR (multiple linear regression) models i.e. M7 (fruit surface) and model M8 (straw) were generated using dissipation half-lives (log10(T1/2)) of pesticides in plants and their different parts. Models were constructed in strict accordance with the guidelines outlined by the Organization for Economic Co-operation and Development (OECD) and extensively validated using globally accepted validation metrics (determination coefficient (R2) = 0.610-0.795, leave-one-out (LOO) cross-validated correlation coefficient (Q2LOO) = 0.520-0.660, MAE-FITTED TRAIN (mean absolute error fitted train) = 0.119-0.148, MAE-LOOTRAIN = 0.132-0.177, predictive R2 or Q2F1 = 0.538-0.567, Q2F2 = 0.500-0.565, MAETEST = 0.122-0.232), confirming their accuracy, reliability, predictivity, and robustness. Lipophilicity, the presence of a cyclomatic ring, suphur, aromatic amine fragments, and chlorine atom fragments are responsible (+ve contribution) for high dissipation half-lives of pesticides in plants. In contrast, hydrophilicity, pyrazine fragments, and rotatable bonds reduce (-ve negative contribution) the dissipation half-lives of pesticides in plants. To address the real-world applicability, the models were employed to screen the PPDB (Pesticide Properties Database) database, which revealed the top 10 pesticides with the highest log(T1/2) in the whole plant and respective parts of the plant body. The present work will aid in developing safer and novel pesticides, regulatory risk assessment, various risk assessments for the sustenance of public health, screening of databases, and data-gap filling.

Comparison of Combined Dissipation Behaviors and Dietary Risk Assessments of Thiamethoxam, Bifenthrin, Dinotefuran, and Their Mixtures in Tea

In the tea-planting process, insecticides are commonly combined, potentially prolonging the pre-harvest interval and heightening the risk of dietary exposure. This study focused on three frequently used insecticides in tea cultivation: thiamethoxam, bifenthrin, and dinotefuran, aiming to investigate their dissipation behaviors and associated dietary risks upon individual and simultaneous application. The dissipation kinetics of thiamethoxam, bifenthrin, and dinotefuran were successfully characterized by first-order kinetics, yielding respective half-lives of 5.44, 9.81, and 10.16 days. Upon joint application, the dissipation half-lives of thiamethoxam and bifenthrin were notably prolonged compared with their individual applications, resulting in final concentrations after 28 days that were correspondingly elevated by 1.41 and 1.29 times. Assessment of the dietary intake risk revealed that the chronic and acute risk quotients associated with thiamethoxam and bifenthrin escalated by 1.44-1.59 times following their combined application. Although dietary risks associated with Tianmuhu white tea, as determined by the exposure assessment model, were deemed acceptable, the cumulative risks stemming from pesticide mixtures across various dietary sources warrant attention. Molecular docking analyses further unveiled that thiamethoxam and bifenthrin competitively bound to glutathione S-transferase (GST) at amino acid residues, notably at the 76th GLU and the 25th PHE, pivotal in the metabolism and absorption of exogenous substances. Moreover, the interactions between P-glycoprotein and pesticides during transport and absorption were likely to influence dissipation behaviors post-joint application. This research offers valuable insights and data support for optimizing joint pesticide application strategies and assessing risks associated with typical pesticides used in tea cultivation.

Causal inference and mechanism for unraveling the removal of four pesticides from lettuce (Lactuca sativa L.) via ultrasonic processing and various immersion solutions

This study explores the reduction of carbamates (CAs) and pyrethroids (PYs) - commonly used pesticides - in lettuce using various immersion solutions and ultrasonic processing. It also examines the role of machine learning and molecular docking in understanding the mechanisms of pesticide reduction. The results revealed that the highest reduction of both CAs and PYs exceeded 80 % on lettuce leaves. In most samples, the reduction increased with the power of ultrasonic processing and processing time. The results of machine learning models (XGBoost and SHAP) showed that during the immersion cleaning of CAs and PYs, as well as during both immersion cleaning and ultrasonic processing of CAs + PYs, the reduction was most influenced by the initial pesticide levels and immersion time. Gas Chromatography-Mass Spectrometry (GC-MS) analysis of lettuce's wax layer identified 24 compounds, including fatty alcohols, fatty acids, fatty acid esters, and triterpenoids. Despite the absence of active sites, the lipophilic nature of long-chain aliphatic compounds aids in pesticide binding, while triterpenoids form strong hydrogen bonds with pesticides, indicating a robust adsorption on the lettuce surface. This study aims to offer insights into the efficient removal of chemical pesticide residues from fruits and vegetables, addressing critical concerns for food safety and human health.

Insecticides chlorantraniliprole and flubendiamide in Aster scaber: Dissipation kinetics, processing effects, and risk assessment

The residue characteristics, processing effects of washing and drying, and dietary risks of chlorantraniliprole (CAP) and flubendiamide (FBD) to Koreans were investigated using Aster scaber in a greenhouse. Following foliar application, the initial FBD residues were 3-10 times higher than those of CAP. However, the biological half-lives were similar at 6.0-8.3 and 6.8-9.9 days for CAP and FBD, respectively. The pre-harvest residue limits (PHRLs) 7 days before harvest, derived from the dissipation rates and maximum residue limits, were 12.2 and 33.2 mg/kg for CAP and FBD, respectively. For the removal of CAP and FBD from A. scaber, washing with a neutral detergent was more effective than running under or dipping in tap water (86.5 % and 66.2 %, respectively). Processing factors in fields I and II were 2.6 and 5.1 for CAP and 2.0 and 5.7 for FBD, respectively. Drying removal efficiencies in fields I and II averaged 46.4 % and 52.3 % for CAP and 48.4 % and 49.2 % for FBD, respectively. Chronic health risk assessments indicated that dietary exposure to CAP and FBD is acceptable for Korean health.

Comparative Biological Half-Life of Penthiopyrad and Tebufenpyrad in Angelica Leaves and Establishment of Pre-Harvest Residue Limits (PHRLs)

To prevent pesticides from exceeding maximum residue limits (MRLs) in crops during export and shipment, it is necessary to manage residue levels during the pre-harvest stages. Therefore, the Republic of Korea establishes pre-harvest residue limits (PHRLs) per crop and pesticide. This study was conducted to set PHRLs for penthiopyrad and tebufenpyrad in angelica leaves, where the exceedance rates of MRLs are expected to be high. The LOQ of the analytical method used was 0.01 mg/kg and it demonstrated good linearity, with a correlation coefficient of 0.999 or higher within the quantitation range of 0.005 to 0.5 mg/kg. The recovery and storage stability accuracy values were in the range of 94.5-111.1%, within the acceptable range (70-120%, RSD ≤ 20%). The matrix effect for both pesticides was in the medium-to-strong range, and it did not significantly impact the quantitative results as a matrix-matched calibration method was employed. Using the validated method, residue concentrations of penthiopyrad 20 (%) EC and tebufenpyrad 10 (%) EC were analyzed. Both pesticides exhibited a decreasing residue trend over time. In Fields 1-3 and their integrated results, the biological half-life was within 2.6-4.0 days for penthiopyrad and 3.0-4.2 days for tebufenpyrad. The minimum value of the regression coefficient in the dissipation curve regression equation was selected as the dissipation constant. The selected dissipation constants for penthiopyrad in Fields 1-3 and their integration were 0.1221, 0.2081, 0.2162, and 0.1960. For tebufenpyrad, the dissipation constants were 0.1451, 0.0960, 0.1725, and 0.1600, respectively. The dissipation constant was used to calculate PHRL per field. Following the principles of the PHRL proposal process, residue levels (%) on PHI dates relative to MRLs were calculated, and fields for proposing PHRLs were selected. For penthiopyrad, since the residue level (%) was less than 20%, the PHRL for Field 3 with the largest dissipation constant was proposed. For tebufenpyrad, as the residue level (%) exceeded 80%, the PHRL proposal could not established. It is deemed necessary to reassess the MRL and 'guidelines for safe use' for tebufenpyrad in angelica leaves.

Systematic approaches to machine learning models for predicting pesticide toxicity

Pesticides play an important role in modern agriculture by protecting crops from pests and diseases. However, the negative consequences of pesticides, such as environmental contamination and adverse effects on human and ecological health, underscore the importance of accurate toxicity predictions. To address this issue, artificial intelligence models have emerged as valuable methods for predicting the toxicity of organic compounds. In this review article, we explore the application of machine learning (ML) for pesticide toxicity prediction. This review provides a detailed summary of recent developments, prediction models, and datasets used for pesticide toxicity prediction. In this analysis, we compared the results of several algorithms that predict the harmfulness of various classes of pesticides. Furthermore, this review article identified emerging trends and areas for future direction, showcasing the transformative potential of machine learning in promoting safer pesticide usage and sustainable agriculture.

Investigation of pesticide residues level on commonly consumed leafy vegetables picked from the central market in Jeddah, Saudi Arabia

This study aimed to investigate the presence of pesticide residues in a variety of commonly consumed leafy vegetables, including Grape leaves, Lettuce, Arugula, Spinach, Purslane, Ocimum, Parsley, Jew's mallow, Celery, Coriander, and Mint. A total of 100 samples were collected from the Central Market of Jeddah, Kingdom of Saudi Arabia. Our methodology involved employing the Quick, Easy, Cheap, Effective, Rugged, and Safe (QuEChERS) extraction method in combination with Liquid Chromatography-Tandem Mass Spectrometry (LC-MS/MS) to analyze a comprehensive database of 237 distinct pesticides. The range for limit of detection (LOD) and limit of quantification (LOQ) of the method were 0.0001 to 0.0014 mg. Kg-1 and 0.0010 to 0.0064 mg. Kg-1 for tested pesticides, respectively. The recoveries were in the range of 70-172.9%, with a relative standard deviation (RSD) of less than 19.0% for all tested pesticides. The results revealed that 60% of the analyzed samples were free from pesticide residues, while 40% exhibited contamination with 17 different pesticide residues. Notably, the most prevalent pesticide detected was Triallate in the Ocimum samples, followed by Metalaxyl in Grape leaves, Mint, and Spinach, and Methomyl in Celery. Approximately 45% of the samples contained pesticide residues that fell below or were equal to the European Union Maximum Residue Levels (EU MRLs), while the remaining 55% exceeded these MRLs. Remarkably, high pesticide concentrations were observed in all Ocimum samples (Triallate, Pyridaben, Hexythiazox, Imidacloprid), 67% of Grape leaves (Metalaxyl, Azoxystrobin, Difenoconazole Isomer), and 40% of Celery (Azoxystrobin, Methomyl). In conclusion, this study sheds light on the contamination levels of commonly consumed domestically produced and purchased leafy vegetables in the Central Market of Jeddah. To ensure food safety and the well-being of consumers, we strongly recommend enhanced scientific assessments and continued monitoring of pesticide usage in agricultural practices.

Learning algorithms estimate pose and detect motor anomalies in flies exposed to minimal doses of a toxicant

Pesticide exposure, even at low doses, can have detrimental effects on ecosystems. This study aimed at validating the use of machine learning for recognizing motor anomalies, produced by minimal insecticide exposure on a model insect species. The Mediterranean fruit fly, Ceratitis capitata (Diptera: Tephritidae), was exposed to food contaminated with low concentrations of Carlina acaulis essential oil (EO). A deep learning approach enabled fly pose estimation on video recordings in a custom-built arena. Five machine learning algorithms were trained on handcrafted features, extracted from the predicted pose, to distinguish treated individuals. Random Forest and K-Nearest Neighbor algorithms best performed, with an area under the receiver operating characteristic (ROC) curve of 0.75 and 0.73, respectively. Both algorithms achieved an accuracy of 0.71. Results show the machine learning potential for detecting sublethal effects arising from insecticide exposure on fly motor behavior, which could also affect other organisms and environmental health.

Quality and risk assessment of lead and cadmium in drinking water for child development centres use in Phatthalung province, Thailand

The purpose of this cross-sectional study and research was to evaluate the health risks to children in relation to the concentration of lead and cadmium in drinking water. Samples were collected between 1 May 2020 and 15 October 2020. Thirty-three child development centres, Phatthalung province, Thailand. Two hundred and ten drinking water samples were taken, consisting of 66 bottled water samples, 66 tap water samples, 66 filtered tap water samples and 12 raw water samples for using in the child development centres. Concentrations of lead and cadmium were identified by graphite furnace atomic absorption spectrometry. The concentration of cadmium in bottled water samples, tap water samples, filtered tap water samples, and raw water samples ranged from nd - 0.0020mg/L, nd - 0.0049 mg/L, nd - 0.0018 mg/L and nd - 0.0049 mg/L. The summation of the total hazard index of bottled water samples, tap water samples, filtered tap water, and raw water samples was less than 1, was considered health-protective. The results will provide the direct evidence needed by child development centres managers to warn learners about the health risk of drinking water among children.

Congener-specific uptake and accumulation of bisphenols in edible plants: Binding to prediction of bioaccumulation by attention mechanism multi-layer perceptron machine learning model

Plant accumulation of phenolic contaminants from agricultural soils can cause human health risks via the food chain. However, experimental and predictive information for plant uptake and accumulation of bisphenol congeners is lacking. In this study, the uptake, translocation, and accumulation of five bisphenols (BPs) in carrot and lettuce plants were investigated through hydroponic culture (duration of 168 h) and soil culture (duration of 42 days) systems. The results suggested a higher bioconcentration factor (BCF) of bisphenol AF (BPAF) in plants than that of the other four BPs. A positive correlation was found between the log BCF and the log Kow of BPs (R2carrot = 0.987, R2lettuce = 0.801, P < 0.05), while the log (translocation factor) exhibited a negative correlation with the log Kow (R2carrot = 0.957, R2lettuce = 0.960, P < 0.05). The results of molecular docking revealed that the lower binding energy of BPAF with glycosyltransferase, glutathione S-transferase, and cytochrome P450 (-4.34, -4.05, and -3.52 kcal/mol) would be responsible for its higher accumulation in plants. Based on the experimental data, an attention mechanism multi-layer perceptron (AM-MLP) model was developed to predict the BCF of eight untested BPs by machine learning, suggesting the relatively high BCF of bisphenol BP, bisphenol PH, and bisphenol TMC (BCFcarrot = 1.37, 1.50, 1.03; BCFlettuce = 1.02, 0.98, 0.67). The prediction of BCF for ever-increasing varieties of BPs by machine learning would reduce repetitive experimental tests and save resources, providing scientific guidance for the production and application of BPs from the perspective of priority pollutants.

ncRNALocate-EL: a multi-label ncRNA subcellular locality prediction model based on ensemble learning

Subcellular localizations of ncRNAs are associated with specific functions. Currently, an increasing number of biological researchers are focusing on computational approaches to identify subcellular localizations of ncRNAs. However, the performance of the existing computational methods is low and needs to be further studied. First, most prediction models are trained with outdated databases. Second, only a few predictors can identify multiple subcellular localizations simultaneously. In this work, we establish three human ncRNA subcellular datasets based on the latest RNALocate, including lncRNA, miRNA and snoRNA, and then we propose a novel multi-label classification model based on ensemble learning called ncRNALocate-EL to identify multi-label subcellular localizations of three ncRNAs. The results show that the ncRNALocate-EL outperforms previous methods. Our method achieved an average precision of 0.709,0.977 and 0.730 on three human ncRNA datasets. The web server of ncRNALocate-EL has been established, which can be accessed at https://bliulab.net/ncRNALocate-EL.

Dynamic modeling of pesticide residue in proso millet under multiple application situations

Proso millet (Panicum miliaceum L.) is a cereal crop with potential resistance to drought and heat stress, making it a promising alternative crop for regions with hot and dry climates. Because of its importance, it is crucial to investigate pesticide residues in proso millet and assess their potential risks to the environment and human health to protect it from insects or pathogens. This study aimed to develop a model for predicting pesticide residues in proso millet using dynamiCROP. The field trials consisted of four plots, with each plot containing three replicates of 10 m2. The applications of pesticides were conducted two or three times for each pesticide. The residual concentrations of the pesticides in the millet grains were quantitatively analyzed using gas and liquid chromatography-tandem mass spectrometry. The dynamiCROP simulation model, which calculates the residual kinetics of pesticides in plant-environment systems, was employed for predicting pesticide residues in proso millet. Crop-specific, environment-specific, and pesticide-specific parameters were utilized to optimize the model. Half-lives of pesticides in grain of proso millet, which were needed to input for dynamiCROP, were estimated using a modified first-order equation. Proso millet-specific parameters were obtained from previous studies. The accuracy of the dynamiCROP model was assessed using statistical criteria, including the coefficient of correlation (R), coefficient of determination (R2), mean absolute error (MAE), relative root mean square error (RRMSE), and root mean square logarithmic error (RMSLE). The model was then validated using additional field trial data, which showed that it could accurately predict pesticide residues in proso millet grain under different environmental conditions. The results demonstrated the accuracy of the model in predicting pesticide residues in proso millet after multiple applications.

Prediction of organophosphorus pesticide adsorption by biochar using ensemble learning algorithms

Machine learning (ML) models have become a potent tool for advancing environmentally conscious research in materials science, allowing the prediction of wastewater treatment efficacy using eco-materials. In this study, we showcase the potential of an advanced decision tree-based ensemble learning algorithm to model the eviction of emerging organophosphate-based pesticidal pollutants in aqueous systems. The model is trained using laboratory-based biochar adsorption data, and it establishes the relationship between independent experimental factors and the % organophosphate pesticide adsorption efficiency as the output parameter. We classified the experimental dataset into input and output parameters to build the model. The input parameters included pyrolysis temperature, solution pH, surface area, pore volume, and initial pesticide concentration. Grid search optimization in Python was employed to train the model using sets of input-output patterns. The results indicated that the XGBoost-based ensemble ML framework provides the best forecast for pesticide adsorption on the biochar matrix, achieving high scores for the regularization coefficient (R2train = 0.998, R2test = 0.981). The concentration of the organophosphorus compound and the morphology of biochar significantly influenced the pesticide adsorption behavior. These findings demonstrate the potential of using ensemble learning algorithms for the balanced design of carbon-enriched biomaterials to remove emerging micropollutants from water effectively.

Bidirectional selection of the functional properties and environmental friendliness of organophosphorus (OP) pesticide derivatives: Design, screening, and mechanism analysis

Organophosphorus pesticides (OPs) are widely used in agricultural production, but the resulting pollution and drug resistance have sparked widespread concern. Therefore, this paper built a model to design OP substitute molecules with high functionality and environmental friendliness, as well as conducted various human health and ecological environment evaluations, synthetic accessibility screening, and easy detection screening. The functionality of the two OP substitute molecules, DIM-100 and DIM-164, increased by 22.79 % and 22.18 %, respectively, and the environmental friendliness increased by 18.07 % and 24.02 %, respectively. The human health risk and ecological, environmental risks were significantly reduced. Both molecules are easy to synthesize, and their detection sensitivity is 9.85 % and 11.24 % higher than that of the target molecule, respectively. Furthermore, significant changes in the distribution of electrons and holes near the C8 and S1 atoms of the OP substitute molecule resulted in easier breakage of the C8-S1 bond, enhancing its photodegradation ability. The charge transfer ability between the atoms of the molecule (as increasing the electron-withdrawing group led to an increase in charge of the P atom) and the volume of the cholinesterase active pocket both affect the functionality of the DIM substitute molecule. That is, the volume of the cholinesterase active pocket of the bee is smaller than that of the brown planthopper and is more affected by the volume of the OP molecule. Furthermore, the mutual verification analysis of the bidirectional selectivity effect of OP substitute molecules between the BayesianRidge model and the 3D-QS(A² + ∀³)R model reveals that the overall charge transfer degree of DIM substitute molecules is the main reason for the increase in the bidirectional selectivity effect.

Comparison of developmental toxicity of benzophenone-3 and its metabolite benzophenone-8 in zebrafish

Benzophenone-3 (BP-3) as one of frequently used organic UV filters has been considered an emerging pollutant due to its toxicities. Benzophenone-8 (BP-8) is one of the main metabolites of BP-3 in organisms. Current reports show that BP-8 may be more toxic than BP-3. However, difference of their toxicities on embryonic development has rarely been reported. In this study, zebrafish embryos were chosen as the target organism to explore the developmental toxicities of BP-3 and BP-8. Non-targeted metabolomic analysis was performed to compare their modes of action. Results showed that BP-8 exposures led to higher bioaccumulation and lower hatching rate of zebrafish larvae than BP-3. Both BP-8 and BP-3 exposures caused behavioral abnormalities of zebrafish larvae, but no significant difference was found between them. At the metabolome level, 1 μg/L BP-3 and 1 μg/L BP-8 exposures altered neuroactive ligand-receptor interaction pathway and FoxO signaling pathway, respectively, which might be involved in the abnormal behaviors in zebrafish larvae. For higher exposure groups (30 and 300 μg/L), both BP-3 and BP-8 exposures changed metabolism of cofactors and vitamins of zebrafish larvae. Exposure of BP-3 altered the metabolism by pantothenate and CoA biosynthesis pathway, while BP-8 exposure changed riboflavin metabolism and folate biosynthesis. The above results indicated different modes of action of BP-3 and BP-8 in zebrafish embryonic development. This study sheds new light to biological hazards of BP-3 due to its metabolism in aquatic organisms.

LC-MS/MS Method Minimizing Matrix Effect for the Analysis of Bifenthrin and Butachlor in Chinese Chives and Its Application for Residual Study

The matrix effect refers to the change in the analytical signal caused by the matrix in which the sample is contained, as well as the impurities that are co-eluted with the target analyte. In crop sample analysis using LC-MS/MS, the matrix effect can affect the quantification results. Chinese chives are likely to exhibit a strong matrix effect when co-extracted with bifenthrin and butachlor due to the presence of phytochemicals and chlorophyll. A novel analytical method was developed to reduce the matrix effects of bifenthrin and butachlor to a negligible level in Chinese chives. The established method had a limit of quantitation of 0.005 mg/kg and correlation coefficients greater than 0.999 within the range of 0.005-0.5 mg/kg. Matrix effects were found to be negligible, with values ranging from -18.8% to 7.2% in four different sources of chives and two leafy vegetables. Compared to conventional analytical methods for the LOQ and matrix effect, the established method demonstrated improved performances. The analytical method was further applied in a residual study in chive fields. The active ingredient of butachlor 5 granule (GR) was not detected after soil admixture application, while that of bifenthrin 1 emulsifiable concentrate (EC) showed a range from 1.002 to 0.087 mg/kg after foliar spraying. The dissipation rate constant (k) of bifenthrin was determined to be 0.115, thus its half-life was calculated to be 6.0 days. From the results, PHI and safety use standards of both pesticides were suggested. The developed analytical method can be applied to accurately determine bifenthrin and butachlor residues in Chinese chives and provides a foundation for further research on the fate and behavior of these pesticides in the environment.